T-GADE: Thermodynamical Generative-AI-Driven Evolution of LLM Artifacts
The utility of thermodynamical selection and validation-based use of retained artifacts are demonstrated, and T-GADE is proposed, which evolves these artifacts by extending thermodynamical genetic algorithms through LLM-based genetic operators and artifact-level diversity evaluation.
Kyoko Ogawa, N. Mori
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